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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Optimal order selection for high order ARX models

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Author(s):
Correa de Godoy, Rodrigo Juliani [1, 2] ; Garcia, Claudio [2]
Total Authors: 2
Affiliation:
[1] Minerva Controls Ltd, Sao Paulo, SP - Brazil
[2] Univ Sao Paulo, Escola Politecn, Av Prof Luciano Gualberto, Travessa Politecn 380, BR-05508010 Sao Paulo, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: DIGITAL SIGNAL PROCESSING; v. 108, JAN 2021.
Web of Science Citations: 0
Abstract

The objective of System Identification is to estimate models that can reproduce the static and dynamic properties of the process being modeled. Some system identification methods require a high order linear model of the process. These models are adopted as the process most reliable description. In some applications, it is necessary to reduce the order of the high order model. An important question is what is the best order of the high order model? This work presents an optimization strategy that maximizes the fitindex in the search for the best high order of SISO and MIMO ARX models. The search for the best time delays related to the high order model is also addressed. The results of the optimization strategy are compared to those obtained by the exhaustive search in two scenarios: comparison of the fitindices of the high order models and comparison of these indices in models with reduced order obtained from the high order models. (C) 2020 Elsevier Inc. All rights reserved. (AU)